System and method for detecting and identifying unmanned aircraft systems

ABSTRACT

Systems, methods, and apparatuses are presented herein for detecting and identifying unmanned aircraft systems (UAS) or drones. The system can include one or more UAS sensor nodes distributed about an area to be monitored. Each UAS sensor node can be communicably coupled to a central server but is able to conduct detection and identification procedures separate from the central server. The UAS sensor node can include a microphone that detects an audio signal generated within the area to be monitored. The node can convert the audio signal into a digital signal, can segment the audio signal, and can pass the signal through a bandpass filter. The node can also conduct a Fourier transform and smooth filtering on the digital audio signal before comparing the signal to multiple stored sample UAS audio signals for known UAS vehicles and motor stresses to determine a likelihood of a match.

FIELD OF THE DISCLOSURE

The present disclosure is generally directed to audio monitoring andevaluation and more particularly to systems and methods for detectingand identifying unmanned aircraft systems (“UAS”), such as droneaircraft, in flight.

BACKGROUND

The vast majority of users of UAS vehicles use them for legitimatepersonal or commercial purposes. However, recent history has shown thata UAS can quickly be converted from a device that is beneficial forcommerce and/or amusement to one that can deliver harm and destructionto a desired location. While certain laws are in place regarding theproper use of UAS vehicles, diligent legislation will not ensurenefarious actors will be completely eliminated or deterred.

Conventional drone detection systems have been designed to monitor anarea for UAS vehicle activity and notify a designated person or entityif a drone is believed to be in the designated area under surveillance.However, this kind of system is limited in its ability to fullycharacterize the threat. For instance, certain brands and models of UASvehicles are able to carry greater payloads than other brands and modelsof UAS vehicles, making them more likely to be able to delivercontraband or destructive devices into the monitored area. Further,certain brands and models of UAS vehicles may include bettertechnological upgrades than other UAS brands and models, which make thembetter suited for precision delivery of contraband or destructivedevices into the monitored area.

In certain situations, knowing the brand and model of the UAS vehiclemay not be sufficient to properly evaluate the likelihood of intent todo harm or cause destruction. In some cases, being able to identify thespecific the UAS vehicle down to the serial number or tail number forthe brand and model of the UAS vehicle may provide the receiving partywith additional information. This additional information may help thereceiving party determine likelihood that the UAS vehicle is enteringthe monitored area with a negative intent.

In addition, knowing additional information about the UAS vehicle couldfurther help the receiving party to determine the intent of the UASvehicle. For example, being able to evaluate the level of strain on theone or more motors driving the UAS vehicle could help determine if theUAS vehicle is carrying a payload that is in addition to the weight ofthe UAS vehicle. More granularly, being able to evaluate the amount orweight of the payload could provide greater insight into the likelymake-up of the payload and the actual potential for damage ordestruction cause by the UAS vehicle or whether it is likely to be onethat is not of concern.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingdrawings. The use of the same reference numerals may indicate similar oridentical items. Various embodiments may utilize elements and/orcomponents other than those illustrated in the drawings, and someelements and/or components may not be present in various embodiments.Elements and/or components in the figures are not necessarily drawn toscale. Throughout this disclosure, depending on the context, singularand plural terminology may be used interchangeably.

FIG. 1 is a simplified block diagram illustrating an example UAS vehicledetection environment, including a central server and one or more UASsensor nodes communicably coupled to the central server and distributedthroughout the detection environment, in accordance with exampleembodiments of the disclosure.

FIG. 2 is a simplified block diagram of a UAS sensor node of FIG. 1, inaccordance with one example embodiment of the disclosure.

FIG. 3 is an example data structure of audio files of UAS vehicle audiosamples stored according to UAS brand, model, identifier number (e.g.,serial number, tail number, registration number), and motor strain levelwithin the UAS sensor node of FIGS. 1 and 2, in accordance with oneexample embodiment of the disclosure.

FIG. 4 is a diagram of different brands and models of UAS vehicles, inaccordance with one example embodiment of the disclosure.

FIG. 5 is a flow chart illustrating an example method for detecting andidentifying UAS vehicles in a monitored area, in accordance with oneexample embodiment of the disclosure.

FIG. 6 is a graphical representation of a one-second digital signalsegment sample, in accordance with one example embodiment of thedisclosure.

FIG. 7 is a graphical representation of a one-second long bandpasseddigital sample, in accordance with one example embodiment of thedisclosure.

FIG. 8 is a graphical representation of a one-second long fast Fouriertransformed digital signal sample, in accordance with one exampleembodiment of the disclosure.

FIG. 9 is a graphical representation of a one-second long smootheddigital signal sample, in accordance with one example embodiment of thedisclosure.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Example embodiments will now be described more fully hereinafter withreference to the accompanying drawings, in which example embodiments areshown. The concepts disclosed herein may, however, be embodied in manydifferent forms and should not be construed as limited to the exampleembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the concepts to those skilled in the art. Likenumbers refer to like, but not necessarily the same or identical,elements throughout.

Certain relationships between dimensions of the UAS detection andidentification system and between features of the UAS detection andidentification system are described herein using the term“substantially.” As used herein, the term “substantially” indicates thateach of the described dimensions or linear descriptions is not a strictboundary or parameter and does not exclude functionally similarvariations therefrom. Unless context or the description indicatesotherwise, the use of the term “substantially” in connection with anumerical parameter indicates that the numerical parameter includesvariations that, using mathematical and industrial principles acceptedin the art (e.g., rounding, measurement or other systematic errors,manufacturing tolerances, etc.), would not vary the least significantdigit.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of the present invention. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

FIG. 1 is a simplified block diagram illustrating an example UAS vehicledetection environment 100, including a central server 135 and one ormore UAS sensor nodes 110 a-n communicably coupled to the central server125 and distributed throughout the detection environment 100, inaccordance with example embodiments of the disclosure. Referring now toFIG. 1, the UAS vehicle detection environment 100 can comprise anybounded or unbounded exterior space 105. In addition, the UAS vehicledetection environment 100 can include covered areas, tunnels, areasbounded on fewer than all sides or any other space that is accessible bya UAS vehicle 130. The exterior space 105 can be located in urban,suburban, or rural settings. Further, the exterior space 105 can besparsely or densely populated and can include humans interacting withinthe space during the time of detection of a UAS vehicle 130.

FIG. 4 is a diagram of different brands and models of UAS vehicles 130,in accordance with one example embodiment of the disclosure. Referringnow to FIGS. 1 and 4, UAS vehicles 130 come in many different shapes andsizes. UAS vehicles 130 can also have many different types of rotors andnumbers of rotors. In addition, UAS vehicles 130 can vary in thepositioning of the rotors, both along the fuselage and whethervertically or horizontally oriented. Some of this variation can beattributed to the intended use of the UAS vehicle 130, while othervariations are more stylistic. While FIG. 4 shows some varieties of UASvehicles 130, the display is not intended to show all of the differenttypes of UAS vehicles 130. For example, FIG. 4 shows a UAS vehicle 405that is generally shaped like a conventional aircraft and includes asingle main rotor along the tail of the vehicle 405. FIG. 4 also shows aUAS vehicles 415 and 420 that are generally shaped like a helicopter andinclude a single main rotor and a tail rotor. In addition, the UASvehicle 410 includes three rotors with at least one rotor positioned ata different elevation. FIG. 4 also presents UAS vehicles 425, 430 havingeight rotors and five rotors respectively.

For each type of UAS vehicle 130, including, but not limited to vehicles405-430, each rotor may be coupled to a motor that is independentlypowered and operated on the vehicle 130. In addition, each rotor can becontrolled separate and distinct from other rotors on the UAS vehicle130. Providing UAS vehicles 130 with independently operated rotors canallow for greater maneuverability of the UAS vehicle 130. UAS vehicles130 can range in weight from one to hundreds or thousands of pounds andhave motors and rotors that can vary in size and shape. In addition,rotors for the different UAS vehicles 130 can be made of differentmaterials, including, but not limited to, plastic, metal, metal alloys,composites, etc.).

The motors driving these UAS vehicles 130 are typically brushless DCmotors. However other motor types are within the scope of thisdisclosure. Each of these motors driving these rotors can generate anaudible noise under operation. In many instances, the noise generated isdifferent not only between brands and models of UAS vehicles 130 butalso from vehicle to vehicle within the same brand. As such, the audiblenoise can act like a fingerprint, individually identifying the specificUAS vehicle 130, not just the type of UAS vehicle, the brand, or themodel.

Throughout this disclosure, reference is made to different types ofaudio signals or acoustic waves that are generated by the motor ormotors on a UAS vehicle 130. The disclosed UAS sensor node 110 convertsthese audio signals into digital sound samples, splits up the samplesinto discreet sizes for comparison, processes each segment of the audiosample into a filtered and fast Fourier transformed sample, conducts aninitial review pass of the sample segment to determine if the digitalsample segment is from a UAS vehicle 130 generally and then does asecond review pass comparing the filtered and fast Fourier transformeddigital audio segment to known UAS vehicle signature audio files todetermine more specific information about the UAS vehicle 130,including, but not limited to, the brand of UAS vehicle, the model ofUAS vehicle, the location of the UAS vehicle when detected, the level ofstrain on the motor and/or an estimated weight being carried by the UASvehicle in addition to the vehicle weight, the potential payload beingcarried by the UAS vehicle, and the time and location of the UAS vehicleor the UAS sensor node 110 when the detection of the UAS vehicleoccurred.

In one example embodiment, the UAS vehicle signature audio files aredigital audio files stored within each individual UAS sensor node 110.By placing the signature audio files in each sensor node 110, it allowsfor a quicker determination as to likelihood of identification of apotential UAS vehicle in the monitored area and reduces thecommunication and computational strain on the central server 115. Inother example embodiments, the UAS vehicle signature audio files can bestored on the central server 115, which can receive the audio signalfrom the UAS sensor node 110 and conduct the filtering, fast Fouriertransforming and analysis of the received audio signal.

In certain example embodiments, at least a portion of the UAS vehiclesignature audio files are generated through testing of different brandsand models of different UAS vehicles 130 and by placing those vehicles130 under different load levels to generate different levels of strainon the motors of those vehicles 130. In addition, at least a portion ofthe UAS vehicle signature audio files are generated “in the field” usingthe system 100 disclosed herein. For example, as different UAS vehicles130 pass through the monitored area 105 and one or more audio signals isdetected and recorded by one or more UAS sensor nodes 110 a-n in themonitored area 105, those fast Fourier transformed and filtered audiosignals can be stored as an audio file in memory 235 (of FIG. 2) locallyat the particular UAS sensor node 110 and/or centrally at the server (sothat the audio file can be transmitted to other UAS sensor nodes 110).In addition to the field-sensed audio file, as much information as canbe determined about the UAS vehicle (e.g., brand, model, ID, motorstress level, payload, etc.) can also be stored in the memory 235 and/ortransmitted to the central server 115 for distribution to one or moreother UAS sensor nodes 110.

As discussed herein, reference may be made to UAS vehicle brands. A UASvehicle brand is made by a particular manufacturer and can includemultiple different types of UAS vehicle models. As discussed herein,reference may also be made to UAS vehicle models. A UAS vehicle model isa specific UAS vehicle product manufactured by a specific entity, thoughit may, and typically will, make multiples of that specific product. TheUAS vehicle model can include, for example, a make, a brand name, aproduct name, a stock keeping unit, etc. Those of ordinary skill in theart will recognize that different entities may manufacture one or manydifferent UAS vehicle models within a UAS vehicle class.

Returning to FIG. 1, the environment 100 can include one or more UASsensor nodes 110 a-n. These UAS sensor nodes 110 a-n (collectively“110”) can be positioned in any form or fashion to sense sound generatedin the monitored space 105. For example, the UAS sensor nodes 110 may bepositioned along the perimeter of the monitored space 105. In anotherexample, the UAS sensor nodes 110 may be positioned centrally within themonitored space 105 and aimed toward the exterior of the monitored space105. In yet another example, the UAS sensor nodes may be clusteredand/or may be positioned throughout the monitored space 105 in an arrayformat. While the example embodiment of FIG. 1 shows four UAS sensornodes 110 a, 110 b, 110 c, and 110 d, this is for example purposes only,as the number of UAS sensor nodes 110 can be any number and can bedependent on the size, location, topography, and other factors of themonitored space.

The example UAS sensor nodes 110 are each configured to individuallysense, detect, and classify UAS vehicles 130 in the monitored space 105.The example UAS sensor node can also be configured to transmit an alertto the central server 115 or to another device, such as a smartphone orhand-held electronic device upon detecting a UAS vehicle 130. In oneexample, each UAS sensor node 110 a-n can include a self-containedapparatus that may be positioned at any location for a particularmonitored space 105, including within a residence, within a building, oroutside. Each UAS sensor node 110 a-n may also include an exteriorcasing that is constructed from metal, hard plastic, soft plastic and/ora combination thereof. This exterior casing may resist leakage to allowfor extended positioning of the UAS sensor node 110 in an outdoorenvironment.

In certain example embodiments, each UAS sensor node 110 a-n can beassigned a unique identifier (e.g., a media access control (“MAC”)address) at the time of manufacture to enable a server or otherelectronic device to determine which UAS sensor node 110 a-n hasdetected and identified a UAS vehicle 130. Alternatively, a user mayprogram or otherwise enter a unique name for each UAS sensor node 110a-n. Each UAS sensor node 110 a-n may then include the unique name orMAC address with any communication to the central server 115 or anotherelectronic device (e.g., smartphone, hand-held electronic device, etc.)in order to uniquely identify the UAS sensor node 110 that istransmitting the information.

In addition, each UAS sensor node 110 a-n may include a GPS transmitter250 (of FIG. 2) to determine and transmit the precise location of theUAS sensor node 110 a-n within a particular environment 100 to thecentral server 115. This GPS data may be used by the central server 115to determine the estimated (based on the location of the particular UASsensor node) location of the UAS vehicle 130 or the precise location(based on triangulation of multiple detections of the UAS vehicle 130from multiple UAS sensor nodes) of the UAS vehicle 130.

The UAS vehicle detection environment 100 can also include a centralserver 115 or computer. The central server 115 can be a standard servercomputer or a cloud-based server computer. The central server 115 mayinclude or otherwise be associated with suitable hardware and/orsoftware for transmitting and receiving data and/or computer-executableinstructions over one or more communication links or networks. Thecentral server 115 may also include any number of processors forprocessing data and executing computer-executable instructions, as wellas other internal and peripheral components currently known in the artor which may be developed in the future. Further, the central server 115may include or be in communication with any number of suitable memorydevices operable to store data and/or computer-executable instructions.For example, the central server 115 can be communicably coupled to oneor more databases or memory storage devices (not shown) to store UASvehicle audio files and detection events received from the one or moreUAS sensor nodes 110 a-n. By executing computer-executable instructions,the central server forms a special-purpose computer or particularmachine. As used herein, the term “computer-readable medium” describesany medium for storing computer-executable instructions.

The example central server 115 may be a computing device that includesany number of server computers, mainframe computers, networkedcomputers, desktop computers, personal computers, mobile devices,smartphones, digital assistants, personal digital assistants, tabletdevices, Internet appliances, application-specific integrated circuits,microcontrollers, minicomputers, and/or any other processor-baseddevices. Additionally, in certain example embodiments, the operationsand/or control of the central server 115 may be distributed amongseveral processing components. In addition to including one or moreprocessors, the central server may further include one or more memorydevices (or memory), one or more input/output (“I/O”) interfaces, andone or more network interfaces. The memory devices may be any suitablememory devices, for example, caches, read-only memory devices, randomaccess memory devices, magnetic storage devices, removable storagedevices, etc. The memory devices may store data, executableinstructions, and/or various program modules utilized by the centralserver 115 and/or the UAS sensor nodes 110 a-n, for example, data files,an operating system (“OS”), and/or example UAS vehicle audio files.

The OS may be a suitable software module that controls the generaloperation of the central server 115. The OS may also facilitate theexecution of other software modules by the one or more processors. TheOS may be any operating system known in the art or which may bedeveloped in the future including, but not limited to, MicrosoftWindows®, Apple OSX™ Apple iOS™, Google Android™, Linux, Unix, or amainframe operating system.

The one or more I/O interfaces may facilitate communication between thecentral server 115 and one or more input/output devices, for example,one or more user interface devices, such as a display 120, keypad,control panel, remote control, mouse, microphone, etc., that facilitateuser interaction with the central server. In certain exampleembodiments, the display 120 may be situated locally with respect to thecentral server 115. In other example embodiments, the display 120 may bepositioned remotely from all or a substantial portion of the centralserver 115. The display 120 can be any form of display known to those ofordinary skill in the art, including, but not limited to, a cathode raytube (CRT) display, a plasma display, a light emitting diode (LED)display, an organic LED display (OLED), a touchscreen display, aheads-up display (HUD), a virtual reality display, or the like.

The central server 115 or computer can be communicably coupled to theone or more UAS sensor nodes 110 a-n. In one example embodiment, thecentral server computer 115 is communicably coupled to each of the oneor more UAS sensor nodes 110 a-n via one or more communications networks125. The network 125 may include one or more independent and/or sharedprivate and/or public networks including the Internet or a publiclyswitched telephone network. In other example embodiments, the centralserver 115 may communicate with each of the UAS sensor nodes 110 a-n viadirect connections and/or communication links.

FIG. 2 is a simplified block diagram of a UAS sensor node 110 a-n ofFIG. 1, in accordance with one example embodiment of the disclosure.Referring now to FIGS. 1 and 2, the example UAS sensor node 110 a-n caninclude a housing for the placement of components included in each node110 a-n. In addition, the UAS sensor node 110 a-n can include one ormore microphones 205. The microphones may be positioned within thehousing, along an exterior of the housing, or provided adjacent to thehousing and communicably coupled to other components within the housing.The example microphone 205 can be a directional or omnidirectionalmicrophone.

The microphone 205 can be configured to have a sensitivity range withina frequency band associated with the sound generated by the motors ofUAS vehicles 130. For example, the microphone 205 can be configured todetect ultrasonic frequency bands. In addition, the microphone 205 mayalso be configured to have an acoustic range to detect sounds from a UASvehicle 130 anywhere in the range of substantially 1 foot tosubstantially 1 mile from the microphone 205 and more particularlywithin one-half mile of the microphone 205.

In certain example embodiments, each UAS sensor node 110 a-n may includemultiple microphones 205. For example, when two or more microphones 205are provided, each microphone 205 may be provided along an exterior ofthe node housing but may face a different direction from the housing.This may allow for an increased arc from the housing at which UASvehicles 130 may be detected.

Each UAS sensor node 110 a-n can also include a sound card 210communicably coupled to the one or more microphones 205 and a processor220. For example embodiments where the UAS sensor node 110 includesmultiple microphones 205, the sound card 210 may be communicably coupledto and service the multiple microphones or a sound card 210 may beprovided for each microphone 205. The example sound card 210 isconfigured to record and digitize an audio signal sensed by the one ormore microphones 205. The sound card 210 may be any type of sound cardknown to those of ordinary skill in the art.

The sound card 210 may be configured to digitize the sound sample into a16-bit, 32-bit, 64-bit, or 128-bit digital signal. In operation, thesound card 210 can include an analog-to-digital converter 215 to convertthe audio signal received from the one or more microphones to a digitalaudio signal. In addition, the sound card 210 may be configured to breakup or divide the digital audio signal into multiple digital audiosegments of a desired length. In one example, the length of each digitalaudio segment is substantially one second. However, in other exampleembodiments, the length of each digital audio segment can be any othertime length including any length within the range of substantially 0.01seconds to substantially one second, and any time length within therange of substantially one second to substantially one minute. Thelength of time for each sample may be a user-configurable parameterselected by the user. The sound card may be configured to transmit theconverted and divided digital audio segments to the processor 220 foradditional processing and evaluation.

Each UAS sensor node 110 a-n can also include one or more processors220. The one or more processors may be communicably coupled to one ormore of the one or more microphones 205, sound card 210,analog-to-digital converter 215, bandpass filter 225, smoothing filter230, and one or more memory or data storage devices 235. The one or moreprocessors may also be operably coupled to a power supply 240 to provideelectrical power for the one or more processors 240. The one or moreprocessors 220 may be implemented as appropriate in hardware, software,firmware, or combinations thereof. Software or firmware implementationsof the one or more processors 220 may include computer-executable ormachine-executable instructions written in any suitable programminglanguage to perform the various functions described herein. Hardwareimplementations of the one or more processors 220 may be configured toexecute computer-executable or machine-executable instructions toperform the various functions described herein. The one or moreprocessors 220 may include, without limitation, a central processingunit (CPU), a digital signal processor (DSP), a reduced instruction setcomputer (RISC), a complex instruction set computer (CISC), aSystem-on-a-Chip (SoC), a microprocessor, a microcontroller, a fieldprogrammable gate array (FPGA), or any combination thereof for handlingspecific data processing functions or tasks. Each UAS sensor node 110a-n may also include a chipset (not shown) for controllingcommunications between the one or more processors 220 and one or more ofthe other components of the UAS sensor node 110.

Each UAS sensor node 110 a-n can also include a bandpass filter 225communicably coupled to the processor 220. In one example, the bandpassfilter is a part of the programming provided in the processor 220 andthe operations of the bandpass filter are conducted by the processor220. In one example embodiment, the bandpass filter 225 is a Butterworthbandpass filter or a maximally flat magnitude filter. The bandpassfilter 225 can be configured to low-pass filter each digital signalsegment processed by the sound card 210. In one example, the cutofffrequencies for the bandpass filter can be anywhere in the range ofsubstantially 1 kilohertz (kHz) to substantially 90 kHz and morepreferably anywhere in the range of substantially 1 kHz to substantially75 kHz, and even more preferably substantially 5 kHz and substantially65 kHz.

Each UAS sensor node 110 a-n can also include a smoothing filter 230communicably coupled to the processor 220. In one example, the smoothingfilter is a part of the programming provided in the processor 220 andthe operations of the smoothing filter are conducted by the processor220. In one example embodiment, the smoothing filter 230 is configuredto smooth out noise in each of the fast Fourier transformed digitalsignal samples generated by the processor 220. For example, thesmoothing filter 230 can include a 25-point running mean filter thatsmooths out noise in the fast Fourier transformed digital signalsamples. In other example embodiments, the smoothing filter 230 canemploy anywhere in the range of a substantially 5-point to asubstantially 50-point running mean filter and more preferably anywherein the range of a 15-point to 35-point running mean filter.

Each UAS sensor node 110 a-n can also include one or more memory orstorage devices 235 communicably coupled to the processor 220. Eachmemory or storage device 235 can be any suitable memory devices, forexample, caches, read-only memory devices, random access memory devices,magnetic storage devices, removable storage devices, etc. The memory orstorage devices 235 can be configured to include instructions forcompleting the processes and methods described herein. Further, thememory or storage devices can include one or more tables, listings, orschedules of UAS vehicle audio sample files that are used for comparisonto the received and processed audio signals to determine if the receivedaudio signals are associated with a UAS device 130.

FIG. 3 is an example data structure 300 of audio files of UAS vehicleaudio samples stored within the memory or data storage devices 235 ofeach UAS sensor node 110 a-n of FIGS. 1 and 2, in accordance with oneexample embodiment of the disclosure. As shown in FIG. 3, the exampledata structure can include an audio file for each record stored in thedata structure 300. Associated with each audio file can be one or morepieces of information specifying the UAS vehicle 130 that generated thesound included on the audio file. In one example embodiment, the one ormore pieces of information can include the brand or manufacture of theUAS vehicle 130, the model of the UAS vehicle 130, a unique identifierfor the UAS vehicle 130 (e.g., serial number, tail number, registrationnumber), the owner of the UAS vehicle 130 (e.g., based on tail number),and motor strain level (e.g., low strain, medium strain, high strain).In addition, or in the alternative, other information can be associatedwith each audio file. For example, instead of, or in addition to, motorstrain level, fields identifying the specific weight of payload for theUAS vehicle, or information specifying the payload (e.g., camera,missile, explosive, etc.) may be included.

The example data structure 300 may be a dynamic data structure 300 inthat it is capable of being constantly updated. For example, as new UASvehicles 130 are created, testing on the vehicles 130 can be conductedto determine new coefficients and intercept data based on the new audiosamples. The new coefficients and intercepts data, which are applied tothe filtered and smoothed audio files collected at a particular UASsensor node 110 a-d as part of the comparison process, can be stored inthe data structure 300 via the central server 115 passing thecoefficients and intercept data and associated information to each UASsensor node 110 a-n via the network 125. Further, as each UAS sensornode 110 a-d collects audio signals that cannot be associated with aparticular UAS vehicle audio sample in the data structure, the newlycollected audio signal can either be transmitted by the particular UASsensor node 110 a-d to the central server 115 via the network 125 forfurther analysis and determination of coefficients and intercept data tobe used in the comparison process and/or can be added to the datastructure 300 as an audio file along with any other information knownabout the particular UAS vehicle 130. As such, each UAS sensor node 110a-n is a learning computer capable of detecting new UAS vehicle signalsand storing them for future comparison. It should be appreciated that inother embodiments, the data structure 300 may include fewer oradditional fields. Moreover, while the data structure 300 is shown as aflat file, in other example embodiments the data structure 300 may behierarchal with a highest level corresponding to UAS vehicle brands, asecond level corresponding to UAS vehicle models, and a lowest levelcorresponding to motor strain levels.

Each UAS sensor node 110 a-n can also include one or more power supplies240 electrically coupled to the processor 220. In addition, the powersupply 240 may be directly or indirectly coupled to any one or more ofthe other components of the UAS sensor node. The power supply 240 can beany currently known or future developed power supply and can beconfigured to provide all of the power needs for the respective UASsensor node 110 a-n. In one example embodiment, the power supply 240 isa direct current (DC) power supply, such as a battery. In this example,the battery can be a rechargeable battery. Further, the UAS sensor node110 can include a solar panel or array, a turbine or the like torecharge the power supply in order to increase the battery life. Inother example embodiments, the power supply 240 is an alternatingcurrent (AC) power supply. While not shown, the node 110 a-n can alsoinclude a combination of power supplies, include back-up power suppliesto further increase battery life for each UAS sensor node 110 a-n whilepositioned out in the field.

FIG. 5 is a flow chart illustrating an example method 500 for detectingand identifying UAS vehicles 130 in a monitored area, such as themonitored area 105, in accordance with one example embodiment of thedisclosure. Referring now to FIGS. 1-3 and 5, the example method 500begins at the start block and proceeds to block 502, where an audiosignal is received by at least one UAS sensor node 110 a-n. In certainexample embodiments, the audio signal can be received at more than oneof the UAS sensor nodes (e.g., 110 a, 110 b, and 110 c) and the signalstrength, the GPS receiver 245 in each node 110 a-c, and the positioningof each UAS sensor node 110 a-c, as determined based on the GPS receiverdata, can be used to triangulate the location of the source of the audiosignal.

In one example, the received audio signal is an analog audio signal. Theaudio signal can be received by the one or more microphones 205 of theUAS sensor node 110. In one example embodiment, each microphone 205 is aUSB microphone that is configured to receive and measure audible andultrasonic sound using a micro electro-mechanical system (MEMS)technology sensing element that operates similar to a human ear drum. Inone example embodiment, the frequency response for the receivingmicrophone 205 is within a range of substantially 2 kHz to substantially95 kHz of sound. The source of the audio signal can be a UAS vehicle130. However, the source of the audio signal could alternatively be ananimal, a person, nature-based sounds (e.g., wind, rain, running water,rustling leaves, etc.), automobiles, other machines, and the like. Onebenefit of the disclosed method is an initial review is conducted todetermine is the source of the audio signal is even likely to be from aUAS vehicle before conducting a more in depth secondary analysis of theaudio signal.

At block 504, the received analog audio signal is converted to a digitalaudio signal. For example, the microphone 205 can pass the analog audiosignal to the sound card 210, which can use an analog-to-digitalconverter 215 to convert the analog audio signal to a digital audiosignal. In one example embodiment, the audio signal is sampled at a rateof 200 kHz with a 16-bit resolution. However, other sample rates between30 kHz to 400 kHz and other resolutions, including, but not limited to,8-bit, 32-bit, 64-bit, and 128-bit resolutions are also within the scopeof this disclosure.

At block 506, the sound card 210 or another portion of the UAS sensornode 110 a-n separates or divides the digital audio signal into digitalsignal segments of a predetermined length. One example of a one-seconddigital signal segment is the digital signal segment 605 shown in thegraph 600 of FIG. 6. In one example embodiment, the predetermined lengthof each digital signal segment 605 is 1 second or 1 Hz. Alternatively,other predetermine lengths for the digital signal segments 605,including those within the range of between substantially 0.01 secondsto substantially one minute, can be used. Separating the digital signalinto smaller segments 605, as disclosed in block 506, is beneficial inthe analysis of the audio signal because it reduces or eliminatesDoppler effects on the audio signal by faster moving UAS vehicles 130.Further, by using the example predetermine length of one second, thesystem is able to generate an updated prediction of UAS vehicle activityin the monitored area 105 every second.

At block 508, a counter variable X, representing each digital signalsegment 605 created in block 506 from the digital audio signal, is setequal to one. At block 510, the first digital signal segment 605 ispassed through a bandpass filter 225 at the UAS sensor node 110 tocreate a bandpassed digital sample X. For example, the processor 220 canpass the first digital signal segment 605 through the bandpass filter225 or can conduct the bandpass filtering on the digital signal segment605. One example of a one-second long bandpassed digital sample X is thebandpassed digital sample 705 shown in the graph 700 of FIG. 7. In oneexample embodiment, the bandpass filter is a Butterworth bandpass filter225 with cutoff frequencies of substantially 5 kHz and substantially 65kHz.

At block 512, a fast Fourier transform is applied to the bandpasseddigital sample X 705. In one example, the fast Fourier transform isconducted on the bandpassed digital signal sample 705 by the processor220 or another portion of the particular UAS sensor node 110. Thebandpassed digital sample X 705 can be Fourier transformed using a 1 Hzbin size, in one example embodiment. Alternatively, any other bin sizecould be used. The Fourier transform of the bandpassed digital sample X,breaks the waveform of the bandpassed digital sample X into an alternaterepresentation characterized by sine and cosine. One example of aone-second long fast Fourier transformed digital signal sample X is thefast Fourier transformed digital signal sample 805 shown in the graph800 of FIG. 8.

At block 514, the fast Fourier transformed digital signal sample X 805is passed through a smoothing filter 230 to create a smoothed digitalsignal sample X. One example of a one-second long smoothed digitalsignal sample X is the smoothed digital signal sample 905 shown in thegraph 900 of FIG. 9. For example, the processor 220 may pass the Fouriertransformed digital signal sample X 805 to the smoothing filter 230 tosmooth out the sample into a sample like that shown at 905 of FIG. 9.Alternatively, the processor 220 may conduct the smoothing process. Inone example, the smoothing process includes passing the Fouriertransformed digital signal sample X 805 though a multi-point runningmean filter to smooth out the noise in sample X. In certain exampleembodiments, the multi-point running mean filter is a 25-point runningmean filter. In another example, the multi-point running means filtercan be anywhere within the range of a substantially 5-point to asubstantially 50-point running mean filter and more preferably anywherein the range of a substantially 15-point to a substantially 35-pointrunning mean filter.

At block 516, the processor 220 compares the smoothed digital signalsample X 905 to sound signals similar to sound files generated by UASvehicles 130. For example, the processor 220 can analyze the smootheddigital signal sample X 905 via a 1-class support vector machinealgorithm with a linear kernel. The 1-class support vector machinealgorithm is an unsupervised learning approach to define a binaryfunction that evaluates to a nonzero value in the input-space regionwhere most of the data lies. In this instance, the 1-class supportvector machine algorithm is based off what is disclosed in Estimatingthe Support of a High-Dimensional Distribution, Bernhard Scholkopf etal., Microsoft Research, Microsoft Corporation, November 1999 (availablevia the Internet at http://www.cs.cmu.edu/˜aarnold/ids/postal.pdf), theentire contents of which is incorporated herein by reference for allpurposes.

In the example embodiment described herein, the input data (a trainingset of filtered, fast Fourier transformed, smoothed audio previouslyrecorded with known UAS vehicles 130) are mapped to feature space usinga linear kernel. Once in feature space, a hyperplane is establishedbetween the origin and the mapped input data such that the marginbetween the origin and these inputs is maximized. Establishing thishyperplane sets a boundary for the testing of new audio data, like thatreceived in block 502 of FIG. 5.

When this model is applied to new processed audio data, such as thesmoothed digital signal sample X 905, determining if a UAS vehicle 130is present is a matter of calculating which side of the hyperplane thesmoothed digital signal sample X 905 finds itself. If the evaluation ofthe smoothed digital signal sample X 905 reveals a positive value, thenthe audio sample received at block 502 is similar enough to thepreviously recorded drone audio such that we can classify that a droneis present. Conversely, a negative value shows the audio is toodifferent from any drone we have seen and is therefore an outlier andlikely not a drone. This approach has worked very well in out processingchain for weeding out audio data from the rest of the environment thatis not a drone.

The support vector machine algorithm acts as an outlier rejection methodfor determining if the smoothed digital signal sample X 905 could notpossibly be originating from a UAS vehicle 130. The objective in block516 is to separate those samples that are potentially originating from aUAS vehicle 130 from everything else in the acoustic background.

At block 518, an inquiry is conducted to determine if the smootheddigital signal sample X is similar to an audio signal from a UAS vehicle130. In one example, the inquiry is conducted by the process and thedetermination is made based on the comparison in block 516. If thesmoothed digital signal sample X is not similar to an audio signal froma UAS vehicle, then the NO branch is followed to block 522. In block522, an inquiry is conducted to determine if there is another digitalsignal segment to evaluate. For example, if the original audio signalreceived at the microphone 205 was ten seconds in length and the signalwas divided up into one second increments, then there would be 10digital signal segments to evaluate for the particular audio signal. Inone example, the determination can be made by the processor 220 of theparticular UAS sensor node 110. If there is not another digital signalsegment to evaluate, the NO branch can be followed back to block 502 toreceive the next audio signal at the microphone. On the other hand, ifthere is another digital signal segment to evaluate, the YES branch canbe followed to block 524, where the counter variable X is incremented byone. The process then returns to block 510 to pass the next digitalsignal segment X through the bandpass filter.

Returning to the inquiry of block 518, if the smoothed digital signalsample X is similar to an audio signal generated by a UAS vehicle 130,the YES branch can be followed to block 526, where the processor 220 canstore the smoothed digital signal sample X in memory 235. Alternatively,the processor 220 can transmit the smoothed digital signal sample X tothe central server 115 which can store it in memory or a databaseassociated with the central server 115. At block 528, sample UAS vehiclesound files are received and/or accessed. For example, the sound filescan be provided by the central computer 115 to each UAS sensor node 110a-n prior to the start of the analysis and can be updated in real-timeor near real-time. In one example, the sample UAS sound files can bestored in the memory 235 of each UAS sensor node 110 a-n in a formsubstantially similar to that shown and described for the data structure300 of FIG. 3.

At block 530, the processor 220 of the UAS sensor node 110 a-n canconduct logistical regression analysis on the smoothed digital sample X905. In one example embodiment, the processor 220 utilizes aone-versus-rest (OVR) logistic regression on the smoothed digital sampleX 905 to determine the identity of a detected UAS vehicle 130. In thisexample, logistic regression seeks to treat class differentiation as a 0or 1 binary problem, where 1 represents an “in-class” sample and 0represents an “out-of-class” sample. Here, OVR logistic regressiontreats each model of UAS vehicle 130 as an individual class and seeks todifferentiate it against all other classes. A separate OVR logisticregression model is trained for each UAS class. For example, if thereare ten classes of UAS vehicle 130, ten separate logistic regression OVRmodels are actually trained. In the training framework, the logisticregression algorithm minimizes a logistic cost function to build anN-dimensional hyperplane mapping between class “0” and class “1”, and aset of coefficients with an intercept is output that defines thishyperplane.

For example, once a new audio sample is gathered, such as in block 502,and processed, such as in blocks 504, 506, and 510-514, a decisionfunction is calculated using the smoothed digital signal sample X 905and the coefficients/intercepts from each of the OVR models stored inthe UAS sensor node 110. The output of the decision function, a scalarvalue, represents the distance between an individual (“one”) UAS vehicleclass and the remaining UAS vehicle classes, where a greater distancerepresents more similarity to a particular UAS vehicle class. Theidentity of the UAS vehicle 130 is thus predicted as the UAS vehicleclass that led to a maximum decision function value. So if you have tenclasses represented in the audio data, you will get ten decisionfunction values.

Class representations when training the logistic regression model can beas granular or as coarse as desired. A class could be a particular UASrepresented by tail number, or as broad as all quadcopters or allfixed-wing vehicles, etc. At block 532, the processor 220 or anotherportion of the UAS sensor node 110 a-n compares the smoothed digitalsample X to each stored UAS sound file in the data structure 300 or inanother location. For example, the decision function values areconverted into probabilistic measures of similarity between one UASvehicle class and the rest of the UAS vehicle classes. Each decisionfunction value described above is input into a logistic function(standard mathematical logistic function). The output of thiscalculation is between 0 and 1. A high value, for example 0.9, wouldrepresent greater similarity to the individual UAS vehicle class (“one”)than the “rest” of the UAS vehicle classes. This calculation is repeatedfor all decision function values and a summation is performed over alloutputs.

At block 534, the processor 220 or another portion of the UAS sensornode 110 a-n generates a probability score for each UAS sample audiofile based on the probability that the smoothed digital sample X matchesthe particular UAS sample audio file. For example, the values of eachindividual UAS vehicle class can then be divided by the sum determinedin the prior block to give the UAS vehicle class relative probabilitiesthat one UAS vehicle 130 is present over the rest of the potential UASvehicle classes.

At block 536, the processor 220 or another portion of the UAS sensornode 110 a-n evaluates all of the probability scores generated at block534 for each of the UAS sample audio files and determines the UAS sampleaudio file that has the highest probability score that the smootheddigital sample X matches the particular UAS sample audio files. Forexample, the greatest probability is always associated with the UASvehicle class having the greatest decision function value. In certainexamples, each of the probabilities can be compared by the processor 220of the UAS sensor node 110 to a predetermined threshold value. If, basedon the comparison, the processor 220 determines that none of theprobabilities are greater than the predetermined threshold value, thenconfidence that the received audio signal in block 502 is from oneparticular UAS vehicle 130 versus one or more other UAS vehicles is lowand the identity of the specific type and payload of the UAS vehicle130, from which the audio signal was received at block 502, is set asindiscernible.

In one example, identifying the highest probability score can beaccomplished by organizing the UAS sample audio files by probabilityscore with regard to the particular smoothed digital sample X.Alternatively, one-on-one matching of probability scores for each UASsample audio file may be conducted by the processor 220 or anotherportion of the UAS sensor node 110 a-n to determine the highest score.While the example embodiment describes identifying the highestprobability score of a match between the UAS sample audio files and thesmoothed digital sample X, in another example embodiment, comparison andgeneration of probability scores could be based on identifying the UASvehicle classes that are least likely to be a match, for which, thelowest probability scores would be identified.

At block 538, the processor 220 or another portion of the UAS sensornode 110 a-n can identify the UAS vehicle details associated with theUAS sample audio file having the highest probability score. For example,the processor 220, based on the identification of the UAS sample audiofile having the highest probability score in block 536, can access thematching record in the data file 300 containing the UAS sample audiofiles and determine the details of the particular UAS vehicle 130. Atblock 540, the processor 220 or another portion of the UAS sensor node110 a-n can determine either the location of the particular UAS sensornode 110 a-n or the estimated location of the UAS vehicle 130 using theGPS receiver 245 for the one or more UAS sensor nodes 110 that detectedthe UAS vehicle. In certain example embodiments, data from only a singleGPS receiver 245 is evaluated to determine an estimated location. Inother example embodiments, GPS receiver data from multiple UAS sensornodes that have detected the UAS vehicle 130 are evaluated andtriangulation techniques are used to estimate the location, direction,and/or speed of the UAS vehicle 130.

At block 542, the processor 220 transmits the UAS vehicle details forthe highest probability UAS sample audio file and location informationto the central server 115. In addition, or in the alternative, the UASvehicle details for the highest probability UAS sample audio file andthe location information can be sent directly to a user's smartphone orother digital display device. At block 544, the identifying informationfor the UAS vehicle 130 associated with the UAS sample audio file havingthe highest probability score is displayed on the display device 120 oranother display device of the user along with the location, speed,and/or direction information for the UAS vehicle 130. In one exampleembodiment, the system can generate a graphical user interface thatincludes a map or grid that includes the monitored area 105. The detailsof the UAS vehicle 130 can be generated on the map or grid at thelocation determined based on the one or more GPS receiver data. Theprocess can then continue to block 522 to determine if there is anothersignal segment to evaluate.

Although unmanned aircraft detection systems methods, functions,components, and parts have been described herein in accordance with theteachings of the present disclosure, the scope of coverage of thispatent is not limited thereto. On the contrary, this patent covers allembodiments of the teachings of the disclosure that fairly fall withinthe scope of permissible equivalents.

Although example embodiments of the disclosure have been described, oneof ordinary skill in the art will recognize that numerous othermodifications and alternative embodiments are within the scope of thedisclosure. Furthermore, while various example implementations andarchitectures have been described in accordance with example embodimentsof the disclosure, one of ordinary skill in the art will appreciate thatnumerous other modifications to the example implementations andarchitectures described herein are also within the scope of thisdisclosure.

Certain aspects of the disclosure are described above with reference toblock and flow diagrams of systems, methods, apparatuses, and/orcomputer program products according to example embodiments. It will beunderstood that one or more blocks of the block diagrams and steps ofthe flow diagrams, and combinations of blocks in the block diagrams andsteps of the flow diagrams, respectively, may be implemented byexecution of computer-executable program instructions. Likewise, someblocks of the block diagrams and steps of the flow diagrams may notnecessarily need to be performed in the order presented, or may notnecessarily need to be performed at all, according to some embodiments.Further, additional components and/or operations beyond those depictedin blocks of the block and/or steps of the flow diagrams may be presentin certain embodiments.

Accordingly, blocks of the block diagrams and steps of the flow diagramssupport combinations of means for performing the specified functions,combinations of elements or steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and stepof the flow diagrams, and combinations of blocks in the block diagramsand steps of the flow diagrams, may be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Computer-executable program instructions may be loaded onto aspecial-purpose computer or other particular machine, a processor, orother programmable data processing apparatus to produce a particularmachine, such that execution of the instructions on the computer,processor, or other programmable data processing apparatus causes one ormore functions or steps specified in the flow diagrams to be performed.These computer program instructions may also be stored in acomputer-readable storage medium (CRSM) that upon execution may direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable storage medium produce an article of manufactureincluding instruction means that implement one or more functions orsteps specified in the flow diagrams. The computer program instructionsmay also be loaded onto a computer or other programmable data processingapparatus to cause a series of operational elements or steps to beperformed on the computer or other programmable apparatus to produce acomputer-implemented process.

Additional types of CRSM that may be present in any of the devicesdescribed herein may include, but are not limited to, programmablerandom access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasableprogrammable read-only memory (EEPROM), flash memory or other memorytechnology, compact disc read-only memory (CD-ROM), digital versatiledisc (DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the information and which can beaccessed. Combinations of any of the above are also included within thescope of CRSM. Alternatively, computer-readable communication media(CRCM) may include computer-readable instructions, program modules, orother data transmitted within a data signal, such as a carrier wave, orother transmission. However, as used herein, CRSM does not include CRCM.

Although example embodiments have been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the disclosure is not necessarily limited to the specific featuresor acts described. Rather, the specific features and acts are disclosedas illustrative forms of implementing the example embodiments.Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainexample embodiments could include, while other example embodiments donot include, certain features, elements, and/or steps. Thus, suchconditional language is not generally intended to imply that features,elements, and/or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without user input or prompting, whether thesefeatures, elements, and/or steps are included or are to be performed inany particular embodiment.

What is claimed is:
 1. An apparatus comprising: at least one microphone;an analog-to-digital converter; at least one data storage devicecomprising a plurality of unmanned aircraft system (UAS) sample audiofiles; and at least one processor configured to: receive an analog audiosignal detected by the at least one microphone from a monitored area;convert the analog audio signal to a digital audio signal by using theanalog-to-digital converter; pass the digital audio signal through abandpass filter to generate a bandpassed digital audio signal; conduct aFourier transform on the bandpassed digital audio signal to generate aFourier transformed digital signal sample; pass the Fourier transformeddigital signal sample through a smoothing filter to generate a smootheddigital signal sample; and compare the smoothed digital signal sample toa plurality of sample UAS audio files for monitoring the smootheddigital signal sample.
 2. The apparatus of claim 1 wherein the at leastone processor is further configured to: calculate, for each of theplurality of sample UAS audio files, a plurality of probability scores,each one of the plurality of probability scores representing alikelihood that the smoothed digital signal sample matches acorresponding one of the plurality of sample UAS audio files; determineone of the plurality of sample UAS audio files with a highestprobability score; identify, based on the determined highest probabilityscore, a matching sample UAS audio file from the plurality of sample UASaudio files; determine, based on the identified matching sample UASaudio file, one or more attributes of a UAS vehicle; and transmit theone or more attributes of the UAS vehicle to a remote computing deviceconfigured to display the one or more attributes.
 3. The apparatus ofclaim 1, wherein the microphone is a directional microphone.
 4. Theapparatus of claim 1, wherein the microphone is an omnidirectionalmicrophone.
 5. The apparatus of claim 1 further comprising: aButterworth bandpass filter communicably coupled to the processor; and asmoothing filter communicably coupled to the processor.
 6. The apparatusof claim 1, further comprising a power supply electrically coupled tothe processor.
 7. A computer-implemented method for detecting andidentifying unmanned aircraft systems (UAS) comprising: receiving, by aUAS sensor node comprising at least one microphone, an analog audiosignal from a monitored area; converting, by the UAS sensor node, theanalog audio signal to a digital audio signal; passing, by the UASsensor node, the digital audio signal through a bandpass filter togenerate a bandpassed digital audio signal; conducting, by the UASsensor node, a Fourier transform on the bandpassed digital audio signalto generate a Fourier transformed digital signal sample; passing, by theUAS sensor node, the Fourier transformed digital signal sample through asmoothing filter to generate a smoothed digital signal sample; andcomparing, by the UAS sensor node, the smoothed digital signal sample toa plurality of sample UAS audio files for monitoring the smootheddigital signal sample.
 8. The computer-implemented method of claim 7,further comprising: calculating, by the UAS sensor node and for each ofthe plurality of sample UAS audio files, a plurality of probabilityscores, each one of the plurality of probability scores representing alikelihood that the smoothed digital signal sample matches acorresponding one of the plurality of sample UAS audio files;determining, by the UAS sensor node, one of the plurality of sample UASaudio files with a highest probability score; identifying, by the UASsensor node and based on the determined highest probability score, amatching sample UAS audio file from the plurality of sample UAS audiofiles; determining, by the UAS sensor node and based on the identifiedmatching sample UAS audio file, one or more attributes of a UAS vehicle;and transmitting, by the UAS sensor node, the one or more attributes ofthe UAS vehicle to a remote computing device configured to display theone or more attributes.
 9. The computer-implemented method of claim 8,wherein the one or more attributes of the UAS vehicle comprise: a UASvehicle brand; a UAS vehicle model; and at least one of a UAS vehicleregistration number, a UAS vehicle tail ID; a motor strain level for theUAS vehicle, a payload weight, and a payload type.
 10. Thecomputer-implemented method of claim 8, further comprising determining,by the UAS sensor node, a location of the UAS vehicle, whereintransmitting the one or more attributes of the UAS vehicle furthercomprises transmitting, by the UAS sensor node, the location of the UASvehicle to the remote computing device.
 11. The computer-implementedmethod of claim 10 further comprising: generating, by the remotecomputing device, a graphical display of an area monitored by the at UASsensor node; and generating, by the remote computing device, a graphicaldepiction of the UAS vehicle on a portion of the graphical display basedat least on the determined location of the UAS vehicle.
 12. Thecomputer-implemented method of claim 7, further comprising dividing, bythe UAS sensor node, the digital audio signal into a plurality ofsegments, each of the plurality of segments having a predeterminedtemporal length.
 13. The computer-implemented method of claim 12,wherein the predetermined length is one second.
 14. Thecomputer-implemented method of claim 7, wherein the bandpass filter is aButterworth bandpass filter.
 15. A non-transitory computer-readablemedium comprising computer-executable instructions that, when executedby one or more processors, configure the one or more processors toperform operations comprising: receive an analog audio signal from amonitored area; convert the analog audio signal to a digital audiosignal; pass the digital audio signal through a bandpass filter togenerate a bandpassed digital audio signal; conduct a Fourier transformon the bandpassed digital audio signal to generate a Fourier transformeddigital signal sample; pass the Fourier transformed digital signalsample through a smoothing filter to generate a smoothed digital signalsample; and compare the smoothed digital signal sample to a plurality ofsample UAS audio files for monitoring the smoothed digital signalsample.
 16. The non-transitory computer-readable medium of claim 15,wherein the operations further comprise: calculate, for each of theplurality of sample UAS audio files, a plurality of probability scores,each one of the plurality of probability scores representing alikelihood that the smoothed digital signal sample matches acorresponding one of the plurality of sample UAS audio files; determineone of the plurality of sample UAS audio files with a highestprobability score; identify, based on the determined highest probabilityscore, a matching sample UAS audio file from the plurality of sample UASaudio files; determine, based on the identified matching sample UASaudio file, one or more attributes of a UAS vehicle; and transmit theone or more attributes of the UAS vehicle to a remote computing deviceconfigured to display the one or more attributes.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the operations furthercomprise: determine a location of the UAS vehicle, wherein transmittingthe one or more attributes of the UAS vehicle further comprisestransmitting, by the UAS sensor node, the location of the UAS vehicle tothe remote computing device.
 18. The non-transitory computer-readablemedium of claim 17, wherein the operations further comprise: generate agraphical display of an area monitored by the at UAS sensor node; andgenerate a graphical depiction of the UAS vehicle on a portion of thegraphical display based at least on the determined location of the UASvehicle.
 19. The non-transitory computer-readable medium of claim 17,wherein the operations further comprise: divide the digital audio signalinto a plurality of segments, each of the plurality of segments having apredetermined temporal length.
 20. The non-transitory computer-readablemedium of claim 15, wherein the bandpass filter is a Butterworthbandpass filter.